@inproceedings{aa5ec7eee3d44a4ab38baf9cfb3362b8,
title = "Gradient based Information Aggregation of GNN for Precoder Learning",
abstract = "Employing graph neural networks (GNNs) for learning the multiuser multi-input multi-output precoder has gained significant attention recently. By modeling the precoder optimization problem in a graph format, GNN can effectively capture the representation of the precoder by leveraging the information aggregated and propagated across the graph. In this paper, we strive to design the information aggregation mechanism of GNN. By analyzing the behavior of the numerical gradient descent algorithm for precoder optimization, we identify the relevant information and the appropriate form for aggregation, enabling us to develop new update equations for GNNs. Simulation results demonstrate the advantages of the proposed GNNs in learning and generalization performance.",
keywords = "GNN, gradient descent, information aggregation, precoder",
author = "Shiyong Chen and Shengqian Han and Yang Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 98th IEEE Vehicular Technology Conference, VTC 2023-Fall ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/VTC2023-Fall60731.2023.10333802",
language = "英语",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings",
address = "美国",
}